Wavelet-enhanced automated fingerprint identification system

ABSTRACT

A method and system for performing automated biological identification. The system including a preprocessing module with a histogram transform for locally and globally enhancing biological data such as fingerprints. An enhancement module with a fast smoothing and enhancement function. A feature extraction module with a fingerprint oriented thinning function. A matching module with a resolution-enhanced Hough transform function for fingerprint registration and matching score function.

BACKGROUND OF THE INVENTION

[0001] 1. Field of The Invention

[0002] The present invention relates to an improved method forcharacterizing, matching, and identifying biologically unique featuressuch as fingerprints and irises. More specifically, it relates tomethods for enhancement of digital images, fast directional convolutionand fingerprint-oriented ridge thinning, matching and identification offingerprints.

[0003] 2. Description of Related Art

[0004] As our society is increasingly electronically-connected,automated personal authentication becomes more important than ever.Traditional techniques, such as those using personal identificationnumbers (PIN) or passwords, will not satisfy demanding securityrequirements as they are incapable of differentiating between anauthorized person and an impostor. In fact, these techniques can onlyverify the correctness of the PIN input by a person, but notauthenticate the true identity of the authorized person.

[0005] To overcome this shortcoming in personal authentication, a numberof biometric techniques have been investigated. Biometric authenticationcapitalizes on some unique bodily features or characteristics of aperson, such as fingerprint, voice, hand geometry, face, palm, and irispattern. Among these biometric features, automated fingerprintidentification system (AFIS) has provided the most popular andsuccessful solution, mainly due to the uniqueness of fingerprints andthe historical legal aspect of fingerprinting for law enforcement.

[0006] A robust and efficient AFIS however, comes with many challenges.The AFIS must be able to differentiate two different fingerprints thatmay be seemingly identical to the untrained eye. The uniqueness of afingerprint is characterized by the finely embedded details (calledminutiae) of the print, and its overall ridge pattern and density. Froma legal standpoint, under Singapore's criminal laws, two fingerprintsthat contain 16 or more reliably matching minutiae are registered asoriginating from the same finger of the same person. As a result, asuccessful AFIS must have strong discrimination power, robustness tocertain degrees of deformation in the fingerprint, and fast (or evenreal-time) processing performance.

[0007] Typically, AFIS includes features such as fingerprint imagepre-enhancement, orientation filtering, ridge thinning, fingerprintregistration and weighted matching score computation. The need forfingerprint image pre-enhancement arises because regardless of theacquisition method and device (either from fingerprint cards, or fromfingerprint readers such as optical sensors, or more recently,semiconductor sensors) fingerprints are susceptible to various forms ofdistortion and noise, including blotches caused by the inputenvironment, skin disease (cuts, and peeling skin), and skin condition(either too wet among younger people, or too dry among elder people). Asa result, fingerprint image enhancement is needed to suppress noise,improve contrast, and accentuate the predominant orientation informationof the fingerprint.

[0008] Orientational filters are generally used for image enhancementaccording to the local directions of fingerprint ridges, which areobtained from the orientation field of the fingerprint image. Prior artfor pre-enhancing includes finding an accurate estimation of theorientation field using some advanced but complicated models andemploying a global enhancement technique (e.g., M. Kass et al.,“Analyzing Oriented Patterns”, Comput. Vis. Graphics Image Process, 37,362-385, 1987. N. Ratha et al., “Adaptive Flow Orientation Based FeatureExtraction in Fingerprint Images”, Pattern Recognition, 28 (11),1657-1672, 1995. Vizcaya et al., “A Nonlinear Orientation Model forGlobal Description of Fingerprints”, Pattern Recognition, 29 (7),1221-1231, 1996.). Nevertheless, these techniques are usuallycomputationally expensive, and hence less suitable for most AFISsolutions that require real-time processing. Another class ofpre-enhancement techniques first accentuates the orientation informationand then estimates the orientation field. The most famous techniquebeing the NIST's FFT-based method (e.g., G. T. Candela, et al.,“PCASYS-A Pattern-Level Classification Automation System ForFingerprints”, National Institute of Standards and Technology, VisualImage Processing Group, Aug. 1995.), and also some other variants of theFFT-based method (e.g., Sherlock et al., “Fingerprint Enhancement byDirectional Fourier Filtering, Proc.” IEE Visual Image Signal Processingvol. 141 (2), 87-94, April 1994).

[0009] After the pre-enhancement, orientation filtering is also commonlyused to further enhance an input fingerprint image. Many filters havebeen designed for fingerprint image enhancement (e.g. Gorman et al., “AnApproach To Fingerprint Filter Design”, Pattern Recognition, Vol. 22(1), 29-38, 1989; B. M. Mehtre, Fingerprint image analysis for automaticidentification, Machine Vision and Applications, 6, 124-139, 1993; Kameiet al., “Image Filter Design For Fingerprint Enhancement”, Proc.International Symposium on Computer Vision, 109-114, 1995; and Maio etal., “Direct Gray-Scale Minutiae Detection In Fingerprints”, IEEETransactions on PAMI, Vol. 19, No. 1, 27-40, January 1997), adopted amethod to filter the image using a class of orientation filters, 1 andthen derive the fingerprint minutiae from the gray-scale image directly.Such a method required intensive computations (e.g., Kasaei et al.,“Fingerprint Feature Enhancement Using Block-Direction On ReconstructedImages”, TENCON '97. IEEE Region 10 Annual Conference, Speech and ImageTechnologies for Computing and Telecommunications., Proceedings of IEEE,vol. 1, 303-306, 1997 attempted to avoid the use of a large class offilters.) To do so, the original image is first rotated to a particulardirection to perform the orientation filtering, and then rotated back tothe original direction. This rotation process introduces loss ofaccuracy due to the quantization noise of rotating a discrete image,which may subsequently result in the detection of false minutiae.

[0010] Regarding ridge thinning, the prior art has consistently shownthat the most effective and robust approach for fingerprint featureextraction is probably using binarization. With this approach, thefingerprint ridges are thinned into binary lines of width of only onepixel before the minutiae are extracted. Some prior art avoidsbinarization by performing the feature extraction process directly onthe grayscale image (e.g., Maio et al., “Direct Gray-Scale MinutiaeDetection In Fingerprints”, IEEE Transactions on PAMI, Vol. 19, No. 1,27-40, January 1997). Such an approach, however, has the drawbacks ofmissing minutiae and inaccurate minutiae position and direction.Further, many powerful thinning algorithms have been developed forChinese character recognitions but they are generally not applicable forthinning ridges in fingerprint images (e.g. Chen et al., “A ModifiedFast Parallel Algorithm For Thinning Digital Patterns”, PatternRecognition Letters, 7, 99-106, 1988; R. W. Zhou, “A Novel Single-PassThinning Algorithm And An Effective Set Of Performance Criteria”,Pattern Recognition Letters, 16, 1267-1275, 1995; and Zhang, “RedundancyOf Parallel Thinning”, Pattern Recognition Letters, Vol. 18,27-35,1997).

[0011] The conventional art includes many methods of fingerprintregistration. Among them, minutia-based methods are the most popularapproaches (e.g. Ratha et al., “A Real-Time Matching System For LargeFingerprint Databases”, IEEE Trans. PAMI, 18 (8), 799-813, Aug., 1996).Such methods make use of the positional and orientational information ofeach minutia (e.g. Ratha et al., “A Real-Time Matching System For LargeFingerprint Databases”, IEEE Trans. PAMI, 18 (8), 799-813, Aug., 1996;Hrechak et al., “Automated Fingerprint Recognition Using StructuralMatching”, Pattern Recognition, 23(8), 893-904, 1990; Wahab et al.,“Novel Approach To Automated Fingerprint Recognition”, Proc. IEE VisualImage Signal Processing, 145(3), 160-166, 1998; and Chang et al., “FastAlgorithm For Point Pattern Matching: Invariant To Translations,Rotations And Scale Changes”, Pattern Recognition, 30(2) 311-320, 1997),or possibly together with a segment of ridge associated with the minutia(e.g. Jain et al., “An Identity-Authentication System UsingFingerprint”, Proc. IEEE, 85(9), 1365-1388, 1997). Some minutia-basedmethods implement registration based on only a few minutiae. Thesemethods are usually simple and fast in computation. However, since thesemethods depend mainly on the local information of a fingerprint, theycannot well handle the influence of fingerprint deformation and mayprovide an unsatisfied registration.

[0012] To overcome this problem, some other methods that exploit theglobal features of the prints have been developed. A typical example ofsuch methods is to use the generalized Hough transform (Ratha et al., “AReal-Time Matching System For Large Fingerprint Databases”, IEEE Trans.PAMI, 18 (8), 799-813, Aug., 1996) to perform the registration. Thisapproach allows consideration of the contribution of all the detectedminutiae in the prints, and is efficient in computation.

[0013] In the weighted matching score computation, the matching score isthe final numerical figure that determines if the input print belongs toan authorized person by comparing the score against a predeterminedsecurity threshold value. Conventionally, the most used formula formatching score computation is given by the ratio of the number ofmatched minutiae to the product of the numbers of the input and templateminutiae. For example, suppose that D minutiae are found to be matchingfor prints P and Q. A matching score is then determined using theequation ${S = \sqrt{\frac{D^{2}}{MN}}},$

[0014] where M and N are the number of the detected minutiae of P and Qrespectively. This way of computing the matching score is simple and hasbeen accepted as reasonably accurate.

[0015] However, such a matching score may be unreliable and inconsistentwith respect to a predetermined threshold. There are situations wheretwo non-matching prints can have a relatively high count of matchingminutia, as compared with a pair of matching prints. This results inrelatively close matching scores, which also means that thediscrimination power to separate between matching and non-matchingprints can be poor for a chosen security threshold value.

[0016] The conventional art includes many methods of fingerprintregistration. Among them, minutia-based methods are the most popularapproaches (e.g. Ratha et al., “A Real-Time Matching System For LargeFingerprint Databases”, IEEE Tans. PAMI, 18 (8), 799-813, Aug., 1996).Such methods make use of the positional and orientational information ofeach minutia (e.g. Ratha et al., “A Real-Time Matching System For LargeFingerprint Databases”, IEEE Tans. PAMI, 18 (8), 799-813, Aug., 1996;Hrechak et al., “Automated Fingerprint Recognition Using StructuralMatching”, Pattern Recognition, 23(8), 893-904, 1990; Wahab et al.,“Novel Approach To Automated Fingerprint Recognition”, Proc. IEE VisualImage Signal Processing, 145(3), 160-166, 1998; and Chang et al., “FastAlgorithm For Point Pattern Matching: Invariant To Translations,Rotations And Scale Changes”, Pattern Recognition, 30(2) 311-320, 1997),or possibly together with a segment of ridge associated with the minutia(e.g. Jain et al., “An Identity-Authentication System UsingFingerprint”, Proc. IEEE, 85(9), 1365-1388, 1997). Some minutia-basedmethods implement registration based on only a few minutiae. Thesemethods are usually simple and fast in computation. However, since thesemethods depend mainly on the local information of a fingerprint, theycannot handle the influence of fingerprint deformation and may providean unsatisfied registration.

[0017] To overcome this problem, some other methods that exploit theglobal features of the prints have been developed. A typical example ofsuch methods is to use the generalized Hough transform (Ratha et al., “AReal-Time Matching System For Large Fingerprint Databases”, IEEE Tans.PAMI, 18 (8), 799-813, Aug., 1996) to perform the registration. Thisapproach allows consideration of the contribution of all the minutiae inthe prints, and is efficient in computation.

[0018] In the weighted matching score computation, the matching score isthe final numerical figure that determines if the input print belongs toan authorized person by comparing the score against a predeterminedsecurity threshold value. Conventionally, the most used formula formatching score computation is given by the ratio of the number ofmatched minutiae to the product of the numbers of the input and templateminutiae. For example, suppose that D minutiae are found to be matchingfor prints P and Q. A matching score is determined using the equation$S = {\sqrt{\frac{D^{2}}{MN}}.}$

[0019] This way of computing the matching score is simple and has beenaccepted as reasonably accurate.

[0020] However, such a matching score may be unreliable and inconsistentwith respect to a predetermined threshold. There are situations wheretwo non-matching prints can have a relatively high count of matchingminutia, as compared with a pair of matching prints. This results inrelatively close matching scores, which also means that thediscrimination power to separate between matching and non-matchingprints can be poor for a chosen security threshold value.

[0021] Therefore a demand exists to provide a method or a system forcharacterizing, matching and identifying fingerprints or otherbiologically unique features, which improves on the above mentionedproblems of AFIS regarding image data pre-enhancement, orientationfiltering, ridge thinning, fingerprint registration and weightedmatching score computation.

SUMMARY OF THE INVENTION

[0022] It is the object of the present invention to provide a method ora system for characterizing, matching and identifying fingerprints orother biologically unique features, which improves on the AFIS andincludes image data pre-enhancement, orientation filtering, ridgethinning, fingerprint registration and weighted matching scorecomputation.

[0023] These advantages are achieved by a biological data matchingsystem including an image reader, which acquires personal biologicaldata; a template of biological data; a pre-enhancing unit adapted topre-enhance the personal biological image data according to local areasof contrast; an image smoothing and enhancement filter for enhancingsaid pre-enhanced image data; an orientation data thinner for removingfalse data in the personal biological image data; a registration unitfor aligning the personal biological image data with the template imagedata and a matching score unit for determining if the biological datamatches the template print. Further, the personal biological data may bea fingerprint, iris, voice, hand geometry, face or palm pattern, etc.

[0024] The advantages are further achieved by a finger print minutiaeextraction method including acquiring fingerprint image data;partitioning the fingerprint image data into at least one data blockcorresponding to a local area of the image; generating a histogramfunction of a contrast level of said image data corresponding to saiddata blocks; and performing a histogram transformation of the histogramfunction. Further, the histogram transformation is adapted to thecontrast level of the local area of the fingerprint image data andpre-enhanced fingerprint image data is generated with local enhancement.

[0025] Further scope of applicability of the present invention willbecome apparent from the detailed description given hereinafter.However, it should be understood that the detailed description andspecific examples, while indicating preferred embodiments of theinvention, are given by way of illustration only, since various changesand modifications within the spirit and scope of the invention willbecome apparent to those skilled in the art from this detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention will become more fully understood from thedetailed description given hereinbelow and the accompanying drawingswhich are given by way of illustration only, and thus are not limitativeof the present invention, and wherein:

[0027]FIG. 1 illustrates a flow chart of the AFIS according to thepresent invention;

[0028]FIG. 2 depicts an 8-neighborhood of pixel P;

[0029]FIG. 3 depicts a table of offset coordinates of directionalconvolution for filter with length 7 and 16 possible directions;

[0030]FIG. 4 depicts a lookup table (LUT₁) for fingerprint-orientedthinning; and

[0031]FIG. 5 depicts a lookup table (LUT₂) for fingerprint-orientedthinning.

DETAILED DESCRIPTION OF THE INVENTION

[0032] The invention is a method and system for performing AFIS withvarious features that contribute to a significant performanceimprovement to two of the most important aspects of an AFIS: efficiency(fast processing), and reliability (accuracy and robustness tovariations in input fingerprints). In particular, these features includea histogram pre-enhancement method, fast smoothing and enhancementmethod for fingerprint images, a fingerprint-oriented thinning methods,a modified Hough transform for fingerprint registration, and an improvedmatching score computation method.

[0033] It is noted that while the specification discusses the inventionin relation to fingerprints, the invention is not limited to anapplication for fingerprints and may also be used for other data imageprocessing and matching such as iris, voice, hand geometry, face andpalm patterns.

[0034] As illustrated in the FIG. 1, the system includes four modules: apreprocessing module 1, an enhancement module 2, a feature extractionmodule 3, and a matching module 4. The preprocessing module 1pre-enhances an input fingerprint image to remove some noise caused bythe fingerprint acquisition device or method 5 and removes the dominantridge directions. The enhancement module 2 further removes noise andaccentuates the desired features of the input fingerprint image so as toprovide a higher quality image for the other processing units. Thefeature extraction module 3 extracts all the fingerprint minutiae thatare unique and consistent features of an individual, and provides thebasis for classification and identification. The matching module 4,implements fingerprint minutiae matching and fingerprint identificationand determines whether or not a fingerprint matches a templatefingerprint. The template fingerprint may be stored in a database 6.

[0035] The preprocessing module 1 includes a histogram transformer 10,dominant ridge direction estimator 11 and coarse segmentation unit 12.The histogram transformer 10 receives finger print images from a fingerprint sensor/reader 5 or other data scanner or acquirer well known inthe art. The dominant ridge direction estimator 11 and coarsesegmentation device 12 receive the transformed fingerprint image data.The coarse segmentation 12 outputs the processed coarse segmentationimage data to the dominant ridge direction estimator 11 which performsthe estimation using the processed coarse segmentation image data andthe transformed fingerprint image data.

[0036] The enhancement module 2 includes a fine segmentation unit 20, anorientation smoothing and enhancing unit 21 and an enhanced fingerprintimage unit 22. The fine segmentation unit 20 and orientation smoothingand enhancing unit 21 receive the image data from the dominant ridgedirection estimator 11 and process the image data in parallel. Both thefine segmentation unit 20 and orientation smoothing and enhancing unit21 deliver processed data to the enhanced fingerprint image unit 22which uses the processed data to further enhance the image data.

[0037] The feature extraction module 3 includes a binalizing grayscaleimage unit 31, a fingerprint oriented thinning unit 32, and a minutiaeextractor 33. The binalizing grayscale image unit 31 receives enhancedimage data from the enhancement module 2 and outputs binalized grayscaleimage data to the fingerprint oriented thinning unit 32. The fingerprintoriented thinning unit 32 outputs thinned image data to the minutiaeextractor 33 for processing.

[0038] The matching module includes a fingerprint registration unit 41and a matching score computation unit 42. The fingerprint registrationunit 41 receives minutia data from the feature extraction module 3 andaligns the current fingerprint against each of the template fingerprintsin the database 6. The matching score computation unit 42 provides anumerical figure that represents the degree of matching between thecurrent fingerprint and a template fingerprint in the database 6. TheAFIS stores the template fingerprints and personal information of eachperson as a record in the database 6.

[0039] During verification or identification, the unique features of aperson's fingerprint are extracted, and searched against the templatefingerprints in the database 6.

[0040] The histogram transform 10 of the preprocessing module 1 performsthe pre-enhancement histogram transformation of the finger print imagedata using a simple but effective method including specially designedhistograms tailored to the statistical information of fingerprintimages. Unlike conventional histogram equalization methods that useconstant functions, a special function is used for global/localenhancement and adapts automatically to the histogram characteristics ofeach input fingerprint. The method can be implemented fast enough foron-line processing, and also gives better performance than approaches inexisting systems such as in Jain et al., “On-Line FingerprintVerification”, IEEE Trans. On Pattern Recognition and MachineIntelligence, 19 (4), 302-314, 1997.

[0041] The histogram transform 10 performs the following pre-enhancementfunctions. The fingerprint image is partitioned into image blocks ofsize S_(b)×S_(b). A block may be formed that encompasses the entirefingerprint image or several blocks may be formed with each blockencompassing only portions of the image. Histograms of pixel intensityon a pixel by pixel basis are generated for each block. Thecorresponding histogram function is also constructed for each block.Assuming that the histogram function for an image block is g(x), thehistogram transformer 10 maps histogram function of the image to aspecific function according to the following mapping$\left. x\mapsto{\arg \quad {\min\limits_{y\quad}\left\{ y \middle| {{\int_{0}^{\pi}{{g(t)}{t}}} < {f(y)}} \right\}}} \right.$

[0042] where f(x) is target histogram function. The target histogramfunction has low value at the mid-point and has a high value at theendpoint of the interval. An example of such function is${{f(x)} = {N_{b} \times {\left( {x - \frac{M}{2}} \right)^{2}/{\int_{0}^{M}{\left( {x - \frac{M}{2}} \right)^{2}{x}}}}}},$

[0043] where M is the maximum value of possible intensity of pixel, suchas 256 for 8-bit per pixel fingerprint images, and N_(b) is the numberof pixels in the image/block, such as 256 for block of size 16×16. Ofcourse, the function f(x) can be optimized to some other suitablefunctions of similar type.

[0044] Such a histogram transformation can be either global for theentire image (by setting the block to be the entire image), or local fora portion of the image (using smaller blocks). When several blocks thatare smaller than the image are used to partition the fingerprint imagedata, a localization property of the transformation operator exists.That is the histograms, corresponding histogram functions andtransformations are generated for a sub-section or local area of thefingerprint image. Thus, the pre-enhancement can adapt to differentcontrast levels at different parts of the image and areas of differingcontrast levels may be processed more specifically. This subsequentlyallows better coarse segmentation of the image according to the mean andvariance values of each image block. Also, image blocks that containactual fingerprint ridges but are still blurred after the processing aremarked as background blocks, which are ignored so as to accelerate thepre-processing module.

[0045] The coarse segmentation 12 of preprocessing module 1 performscoarse segmentation on every S_(b)×S_(b) block. The coarse segmentationunit 12 identifies a block as a foreground block or a background blockby comparing the mean value and variance of the block with predeterminedthreshold and generates tags of every block. The tags have values 1 fora foreground block or 0 for a background block.

[0046] The dominant ridge direction estimator 11 in preprocessing module1 performs dominant ridge direction estimation according to an algorithmand using the tags generated by coarse segmentation unit 12 and thepre-enhanced image data generated by histogram transform 10. Only blockswith a tag value of 1 are processed in the dominant ridge directionestimator 11. The output of preprocessing module 1 is the index of oneof 16 discrete directions generated by dominant ridge directionestimator 12 and output image generated by histogram transform 10. Anexample of a dominant ridge estimation algorithm is that used in K.Jain, and H. Lin, “On-Line Fingerprint Verification, IEEE Trans. OnPattern Recognition And Machine Intelligence”, 19 (4), 302-314, 1997.Other suitable algorithms and processing methods may also be used.

[0047] Using the output of the pre-processing module 1, the finesegmentation unit of enhancement module 2 analyzes the smoothness ofdiscrete direction of each foreground block and the corresponding8-neighbourhood blocks of the foreground block. The 8 neighbors of theforeground block are arranged the as same as the pixels are depicted inFIG. 2. If the difference of discrete direction of a foreground blockbetween the average direction of 8-neighbor blocks is greater than apredetermined threshold, the foreground block is segmented as abackground block and the tag of this block is assigned to 0.

[0048] The orientation smoothing and enhancing unit 21 of theenhancement module 2 performs orientation filtering using twoconvolution processes. A smoothing process and an enhancing process isimposed on every foreground block image. First, the smoothingconvolution for all foreground blocks occurs. Then, the foregroundblocks are enhanced. The convolution is a directional convolution for a2-dimensional digital image, and includes a convolution of the filter(low pass filter for smoothing and high pass filter for enhancing,respectively) with the current block image data by a directional filter.The convolution is implemented by imposing on every pixel within theblock the following algorithm:${g\left( {i,j,k} \right)} = {\sum\limits_{l = 1}^{M}{{f\left( {{i + {y_{offset}(l)}},{j + {x_{offset}(l)}}} \right)} \times {h(l)}}}$

[0049] where g(i,j,k) is the output of image intensity at location(i,j); k is index of the dominant direction α_(k)=k×π/16 of currentblock for smoothing processing and k is index of the perpendiculardirection of dominant direction of current block for enhancingprocessing, and h(l) is the low pass filter with 7-tap for smoothingconvolution and the high pass filter with 7-tap for enhancingconvolution, respectively. The offset coordinates (x_(offset),y_(offset)) corresponding to discrete direction α_(k) are listed in FIG.3.

[0050] The enhanced fingerprint image unit 22 sets all pixels ofbackground block marked by fine segmentation unit 20 to 0 the output oforientation smoothing and enhancing unit 21 and produces an enhancedfingerprint image.

[0051] In the feature extraction module 3, the binalizing grayscaleimage unit 31 and minutiae extractor 33 are standard algorithms used inimage processing (e.g. S. Kasaei, and M. Deriche, “Fingerprint FeatureEnhancement Using Block-Direction On Reconstructed Images”, TENCON '97.IEEE Region 10 Annual Conference. Speech and Image Technologies forComputing and Telecommunications., Proceedings of IEEE, vol. 1, 303-306,1997.

[0052] The input and output of binalizing grayscale image unit 31 aregrayscale image and a black/white image, respectively. The input ofminutiae extractor 33 is a black/white image with one-pixel widthcurve/ridge and the output of minutiae extractor 33 is a set of minutiaeinformation in which there are attributes of one minutiae including thecoordinate of minutiae and the direction of this minutiae.

[0053] The fingerprint oriented thinning unit 32 of the featureextraction module 3 processes the output of the binalizing grayscaleimage unit 31 giving special consideration to the unique properties offingerprint ridges and minutiae. Unlike character recognitionapplications, a critical problem in fingerprint recognition applicationis the formation of false connections that incorrectly link up twoadjacent disjoint ridges. These false connections need to be removed, asthey will subsequently introduce false minutia and impair the accuracyof the matching module 4. The invention incorporates a set of rules thatredefine the behavior of the thinning algorithm such that there are muchfewer false connections after the thinning process.

[0054] The fingerprint oriented thinning unit 32 processes fingerprintdata by using an algorithm to apply LUT₁ and LUT₂, as shown in FIGS. 4and 5, respectively, to each pixel foreground pixel P. An example of analgorithm that may be used is found in Chen et al., “A Modified FastParallel Algorithms For Thinning Digital Patterns”, Pattern RecognitionLetters, 7, 99-106, 1988. Other suitable algorithms may also be used.

[0055] The LUTs are formed using the following rules. Let A(P) be thenumber of 0-1 (binary transition) patterns in the order set P₁, P₂, P₃,P₄, P₅, P₆, P₇, P₈, P₁, where P_(i), i=1, . . . ,8, are the 8-neighborsof the foreground pixel P in a clockwise direction (see FIG. 2), andB(P) is the number of nonzero neighbors of P. The following rules areapplied.

[0056] For 2≦B(P)≦7, we choose either

[0057] 1. A(P)=1,

[0058] If P₁*P₃*P₅=0 and P₃*P₅*P₇=0 then LUT₁(P)=0;

[0059] If P₁*P₃*P₇=0 and P₁*P₅*P₇=0 then LUT₂(P)=0; or

[0060] 2. A(P)=2,

[0061] If P₁*P₃=1 and P₅+P₆+P₇=0 then LUT₁(P)=0;

[0062] If P₃*P₅=1 and P₁ +P ₇+P₈=0 then LUT₁(P)=0;

[0063] If P₁*P₇=1 and P₃+P₄+P₅=0 then LUT₂(P)=0;

[0064] If P₅*P₇=1 and P₁+P₂+P₃=0 then LUT₂(P)=0.

[0065] The following new rules are incorporated into the new algorithm:

[0066] If P₁*P₇*P₈=1 and P₂+P₆>0 and P₃+P₅=0 then LUT₁(P)=0;

[0067] If P₅*P₆*P₇=1 and P₄+P₈>0 and P₁+P₃=0 then LUT₁(P)=0;

[0068] If P₁*P₂*P₃=1 and P₄+P₈>0 and P₅+P₇=0 then LUT₂(P)=0; and

[0069] If P₃*P₄*P₅=1 and P₂+P₆>0 and P₁+P₇=0 then LUT₂(P)=0.

[0070] The algorithms listed above in rule groups 1 and 2 can be foundin Chen et al., “A Modified Fast Parallel Algorithm For Thinning DigitalPatterns”, Pattern Recognition Letters, 7, 99-106, 1988. Rule groups 1and 3 are designed for thinning of character images while the new rulesare designed for the thinning of biological data such as fingerprints.The set of new rules not only reduces the number of false connections,but also significantly cuts down the number of computations becauseduring each iteration, the fingerprint oriented thinning unit 32 iscapable of removing more pixels than other conventional methods.

[0071] The fingerprint oriented thinning unit 32 performs the followingiteration on each foreground pixel P. If the result of the lookup table,LUT₁, as shown in FIG. 4 is equal to zero, then the pixel P is removedby classifying it as background. The same procedure is then repeatedusing another lookup table, LUT₂, as shown in FIG. 5. This process isiterated on all foreground pixels until no pixels can be removed.

[0072] The fingerprint registration unit 41 in the matching module 4 hassignificant affect on the performance of the entire AFIS system. Duringregistration, the current fingerprint is aligned against each templatefingerprint in the database 6. The database 6 may contain one or morethan one templates. The invention performs fingerprint imageregistration using a resolution-enhanced generalized Hough transform.

[0073] The generalized Hough transform is defined as follows (Ratha etal., “A Real-Time Matching System For Large Fingerprint Databases”, IEEETrans. PAMI, 18 (8), 799-813, August 1996): Let P and Q denote theminutia data sets extracted from an input fingerprint image and atemplate, respectively. Usually, P and Q can be organized as P={(p_(x)¹, p_(y) ¹, α¹), . . . , (p_(x) ^(M), p_(y) ^(M), α^(M))}, and Q={(q_(x)¹, q_(y) ¹, β¹), . . . , (q_(x) ^(N), q_(y) ^(N), β^(N))}, where (p_(x)^(i), p_(y) ^(i), α^(i)) and (q_(x) ^(j), q_(y) ^(j), β^(j)) are thefeatures, i.e., the position and orientation associated with the ithminutia in P and the jth minutia in Q, M and N are the number of thedetected minutiae of P and Q, respectively. The generalized Houghtransform aligns P against Q by determining the translation parametersΔx in x axis and Δy in y axis, and the rotation parameter θ. It firstdiscretizes the parameter space into a lattice with θε{θ₁, . . . ,θ_(I)}, Δx ε{Δx₁, . . . , Δx_(J)}, and Δy ε{Δy₁, . . . , Δy_(K)}. Eachcombination {Δx_(i), Δy_(j), θ_(k)} is called a lattice bin. Then, foreach minutia in P and every minutia in Q, it computes the transformationparameters {Δx, Δy, θ} by

∂=β^(j)−α^(i)  (1)

[0074] and $\begin{matrix}{\begin{pmatrix}{\Delta \quad x} \\{\Delta \quad y}\end{pmatrix} = {\begin{pmatrix}q_{x}^{j} \\q_{y}^{j}\end{pmatrix} - {\begin{pmatrix}{\cos \quad \theta} & {{- \sin}\quad \theta} \\{\sin \quad \theta} & {\cos \quad \theta}\end{pmatrix}\quad \begin{pmatrix}p_{x}^{i} \\p_{y}^{i}\end{pmatrix}}}} & (2)\end{matrix}$

[0075] and quantizes them into the above mentioned lattice. Each set ofthe quantized parameters obtained is said to be one ‘evidence’ of thecorresponding lattice bin. The generalized Hough transform counts thenumber of evidences for each bin in the lattice and finally specifiesthe parameters of the lattice bin that corresponds to the maximum numberof evidences as the parameters for the (actual) registration.

[0076] While the generalized Hough transform is efficient in computationand works well in many cases, even on partial information of prints, itsuffers from an inherent problem that limits its performance. To makethe registration accurate, it is desirable to use small size of latticebins. This, however, will result in a low maximum evidence count, whichmeans that the alignment will be less reliable. On the contrary,increasing the lattice bin size will lead to poor spatial resolution andthereby low registration accuracy.

[0077] In the inventive AFIS, the standard generalized Hough transformis modified such that it can simultaneously overcome the above-mentionedproblem, and yet retain the major advantages of the Hough transform. Themodification is as follows. 1) A sufficiently large lattice bin size ischosen experimentally to ensure that enough evidences can be accumulatedwithin a bin. 2) The number of evidences for each of the bins of thelattice is counted as is done in the generalized Hough transform. 3) Thelattice is shifted in the x and y directions at a predetermined stepsize, and further, the number of evidences for each bin of the shiftedlattice is counted. The shifting and counting processes are repeated andstops until a bin at the final position of shifting is overlappedcompletely with its diagonal neighbor at the position before theshifting process. Note that shifting the lattice essentially partitionseach bin into blocks. In a direction, each block has the same size asthe shift step. 4) For each shift, each block is assigned a number equalto the counts of evidence of the bin in which the block is contained. 5)All such numbers of the overlapped blocks for each block position aresummed, and the transform parameters are specified as the positionparameters of the block that corresponds to the maximum sum.

[0078] Testing was performed for the various bin and shifting step sizeswith as many prints as possible so as to determine the best translationand rotation parameters. Currently, the bin size is Δx=16 pix, Δy=16pix, and θ=10°. The step size of shifting is 4 pix in both the x and ydirections. These values however may be changed because they arerelevant to the fingerprint sensor used in a system.

[0079] The advantage of this approach, as compared with the ordinarygeneralized Hough transform, is that the registration accuracy andreliability now are determined by shift step and bin size, respectively,which overcomes the shortcomings mentioned previously. The accuracy ofregistration is increased by decreasing the shift step size whilesimultaneously maintaining the reliability. Although this approach addssome computations, application tests verified that it is suitable forreal-time applications.

[0080] The matching score computation unit 42 provides the finalnumerical figure that determines if the input print belongs to anauthorized person by comparing the score against a predeterminedsecurity threshold value. To overcome the problems in the traditionalart, the invention incorporates a method of computing a matching scorethat is more reliable and has a much improved discrimination capability.This method exploits the features of the above approach for fingerprintregistration using shifted lattices. It was found that although thenumber of matched minutia between two non-matching prints can berelatively high, the distribution of the evidence counts over thelattices can be largely different from that of two matching prints. Fornon-matching prints, the maximum value of evidence counts is low whilethe variance of the parameters of the lattice bins corresponding to themaximum evidence counts is large (usually more than one bins have themaximum evidence counts). In addition to using the number of matchedminutia, the method takes into consideration these two factors in thecomputation of the final matching score.

[0081] Sigmoid nonlinear functions are used to weigh the contribution ofeach of the three factors. $\begin{matrix}{{S = {\sigma \quad \left( {w_{m}\left( {m - m_{0}} \right)} \right)\quad {\sigma \left( {w_{v}\left( \frac{1}{v - v_{0}} \right)} \right)}\sigma \quad \left( {w_{D}\left( {D - D_{0}} \right)} \right)\sqrt{\frac{D^{2}}{MN}}}},} & (3)\end{matrix}$

[0082] where m is the maximum value of evidence counts, v is thevariance of the parameters of the lattice bins corresponding to themaximum evidence counts, D is the number of the matched minutiae, M andN are the number of the detected minutiae in the current and templateprints, and w_(m), w_(v), w_(D), m₀, v₀ and D₀ are prespecifiedparameters determined by experiments, respectively.${\sigma \quad (\bullet)} - \frac{1}{1 + {\exp \quad \left( {- (\bullet)} \right)}}$

[0083] is the sigmoid function.

[0084] Experimental tests confirmed that this formulation provides muchbetter discrimination between matched and unmatched pairs than thetraditional computation of matching score. Also, this improvement couldbe achieved with almost no extra computations.

[0085] Experimental tests confirmed that this formulation provides muchbetter discrimination between matched and unmatched pairs than thetraditional computation of matching score. Also, this improvement couldbe achieved with almost no extra computations.

[0086] The declared AFIS system can be used for both one-to-one matchingas well as for one-to-many matching against database prints ofauthorized persons. As an example of the matching speed, the correctidentification of an individual from a database of about 100 personswill take less than 1.5 seconds, while a one-to-one matching will takeonly about 0.4 second (using a Pentium Pro 200 processor). The system isalso very robust to variations in input fingerprints. For example, thesystem can still correctly authenticate a person with an inputfingerprint that is of low captured quality, and with some portions ofthe print removed.

[0087] Some applications of the invention include but are not limited tothe following: secured transactions (e.g., using credit card forInternet banking and retailing, and for user authentication in automatedteller machines); secured access control (e.g., lift and door access inbuildings, and computer or network access without using passwords orcards); secured database systems (e.g., medical database of patients,nation-wide database for immigration and identification control;time-stamping database of workers in a company, and logistic databasefor controlling equipment checkout, or for monitoring movements ofprisoners).

[0088] The invention being thus described, it will be obvious that thesame may be varied in many ways. Such variations are not to be regardedas a departure from the spirit and scope of the invention, and all suchmodifications as would be obvious to one skilled in the art are intendedto be included within the scope of the following claims.

What is claimed is:
 1. A finger print minutiae extraction methodcomprising: acquiring fingerprint image data; partitioning saidfingerprint image data into at least one data block corresponding to alocal area of said image data; generating a histogram function of acontrast level of said image data corresponding to said data blocks; andperforming a histogram transformation of said histogram function,wherein said histogram transformation is adapted to the contrast levelof said local area of said fingerprint image data and pre-enhancedfingerprint image data is generated with local enhancement.
 2. Themethod of claim 1 , further comprising: partitioning said fingerprintimage data into a plurality of data blocks, each of said plurality ofblocks corresponding to a different local area of said image data and atleast one of said plurality blocks having a contrast level differentthan a second of said plurality of data blocks, wherein said histogramtransformation is adapted to said different contrast levels of saidplurality of blocks and pre-enhanced fingerprint image data is generatedwith local enhancement for a plurality of local areas of said imagedata.
 3. The method of claim 1 , wherein said histogram transformationincludes using an objective function with a relatively high value atboth endpoints of an intensity interval and a relatively low value at amiddle of said intensity interval.
 4. The method of claim 1 , whereinnoise and distortions in said image data are reduced.
 5. The method ofclaim 1 , wherein said histogram transform maps said histogram functionto a specific function according to a mapping algorithm including$\left. x\mapsto{\arg \quad {\min\limits_{y\quad}\left\{ y \middle| {{\int_{0}^{\pi}{{g(t)}{t}}} < {f(y)}} \right\}}} \right.$

wherein f(x) is a target histogram function and said target histogramfunction has low value at the mid-point and has a high value at theendpoint of the interval.
 6. The method of claim 1 , further comprising:performing orientation filtering on said pre-enhanced data usingdirectional convolution for two dimensional digital image processing,wherein said pre-enhanced image data is smoothed and enhanced.
 7. Themethod of claim 6 , wherein the following algorithm is used in saidorientation filtering${g\left( {i,j,k} \right)} = {\sum\limits_{l = 1}^{M}{{f\left( {{i + {y_{offset}(l)}},{j + {x_{offset}(l)}}} \right)} \times {{h(l)}.}}}$


8. The method of claim 1 , further comprising: thinning said fingerprintimage data to remove false connections of ridges in said data, whereinsaid thinning includes applying a first table and a second table to aplurality of pixels using an algorithm.
 9. The method of claim 1 ,further comprising generating a first table and a second table usingrules for character data and biological data.
 10. The method of claim 9, wherein said rules for biological data include If P₁*P₇*P₈=1 andP₂+P₆>0 and P₃+P₅=0 then LUT₁(P)=0; If P₅*P₆*P₇=1 and P₄+P₈>0 andP₁+P₃=0 then LUT₁(P)=0; If P₁*P₂*P₃=1 and P₄+P₈>0 and P₅+P₇=0 thenLUT₂(P)=0; and If P₃*P₄*P₅=1 and P₂+P₆>0 and P₁+P₇=0 then LUT₂(P)=0,wherein A(P) is a number of 0-1 patterns in an order set P₁, P₂, P₃, P₄,P₅, P₆, P₇, P₈, P₁, where P_(i), i=1, . . . , 8, are 8-neighbors of apixel in a clockwise direction, and B(P) is a number of nonzeroneighbors of P.
 11. A method for fingerprint registration andverification from minutiae comprising: performing a Hough transform onfingerprint image data and generating evidences in lattice bins;counting the evidences accumulated in said lattice bin; shifting alattice; determining the number of evidences in each bin of said shiftedlattice; repeating said shifting and counting in each direction of saidlattice until a bin is completely overlapped with its diagonal neighbor,wherein shifting the lattice enhances the spatial resolution of theHough transform.
 12. The method of claim 10 , wherein said shifting saidlattice occurs at a predetermined step size.
 13. The method of claim 10, wherein said shifting the lattice partitions each bin into blocks,each block is assigned a number equal to the number of evidences in thecorresponding bin, the numbers of the overlapped blocks are summed andtransform parameters are specified using the block that corresponds tothe highest sum.
 14. The method of claim 10 , further comprising:determining the maximum number of evidence counts in the bins;determining transformation parameters corresponding to the bins with themaximum evidence counts; determining the variance of saidtransformational parameters; determining a matching score of afingerprint image and a template fingerprint image based on saidvariance of the transformational parameters and said maximum number ofcounts.
 15. The method of claim 13 , wherein the matching score isdetermined using a sigmoid nonlinear function.
 16. A system forbiological data matching comprising: an image reader for acquiringpersonal biological image data; a template of biological image data; apre-enhancing unit adapted to pre-enhance said personal biological imagedata according to local and global areas of contrast; an image smoothingand enhancement filter for enhancing said pre-enhanced image data; anorientation data thinner for removing false data in said personalbiological image data; a registration unit for aligning said personalbiological image data with said template image data; and a matchingscore generating unit for determining if said biological data matchessaid template print.
 17. The system of claim 15 , wherein said personalbiological image data and said temple image data include a fingerprint,iris, voice, hand geometry, face, or palm pattern.
 18. The system ofclaim 15 , further comprising: a database including a plurality oftemplates of biological image data, wherein said system determines whichtemplate of said plurality of templates in said database matches saidpersonal biological image data.
 19. The system of claim 15 , whereinsaid registration unit aligns said image data with said template using aHough transform and shifts a lattice to enhance the spatial resolutionof the Hough transform.
 20. The system of claim 15 , wherein saidpre-enhancing unit enhances local areas of contrast by partitioning saidimage data into image data blocks, generating a histogram function of acontrast level of said image data corresponding to said data blocks, andperforming a histogram transformation of said histogram function.